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A minimum information model a virtual biobank based on the Minimum Information About BIobank Data Sharing (MIABIS)

We feel that Data Science is not prioritised in our current research model, and the current consultation process has not sufficiently addressed the importance of Data Science for fast-tracking, and delivering impact from, Aotearoa/New Zealand’s research. Data underpins all research carried out in Aotearoa/New Zealand and supporting those that work with data is imperative. The current model limits the potential of researchers to bring cutting-edge solutions to the problems faced by Aotearoa/New Zealand. The result of Aotearoa/New Zealand’s current research funding model is that Data Science has been regarded as supporting science and research, rather than a field in its own right. We feel that a national strategy is needed to grow Data Science into a world-class field by supporting infrastructure, capability and best practices in security, diversity inclusion and process design.

The Policy Action Network is a project in the Human Sciences Research Council which aims to connect government policy processes with research, data and stakeholder knowledge. As part of this work we are interested in how government decision-making could benefit from more open forms of data sharing and reuse. This raises a number of questions: What are possible pathways to decision-support? How can enhancing and publishing meta-data help? What would we like to see in data governance practices, standards and business models? This talk will explore early stages of a project to develop a health systems evidence map and initial thoughts around the use of contracting data, and some ideas on how these could be taken forward.

Vast amounts of sensitive personal information are collected by companies, institutions and governments. A key technological challenge is how to effectively extract knowledge from data while preserving the privacy of the individuals involved. In this dissertation, we address this challenge from the perspective of privacy-preserving data collection and analysis. We focus on investigation of a technique called local differential privacy (LDP) and studied several aspects of it. In particular, the thesis serves as a comprehensive study of multiple aspects of the LDP field. We investigated the following seven problems: (1) We studied LDP primitives, i.e., the basic mechanisms that are used to build LDP protocols. (2) We then studied the problem when the domain size is very big (e.g., larger than $2^{32$), where finding the values with high frequency is a challenge, because one needs to enumerate through all values. (3) Another interesting setting is when each user possesses a set of values, instead of a single private value. (4) With the basic problems visited, we then aim to make the LDP protocols practical for real-world scenarios. We investigated the case where each user's data is high-dimensional (e.g., in the census survey, each user has multiple questions to answer), and the goal is to recover the joint distribution among the attributes. (5) We also built a system for companies to issue SQL queries over the data protected under LDP, where each user is associated with some public weights and holds some private values; an LDP version of the values is sent to the server from each user. (6) To further increase the accuracy of LDP, we study how to add post-processing steps to protocols to make them consistent while achieving high accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values. (7) Finally, we investigate a different model of LDP which is called the shuffler model. While users still use LDP algorithms to report their sensitive data, now there exists a semi-trusted shuffler that shuffles the users' reports and then send them to the server. This model provides better utility but at the cost of requiring more trust that the shuffler should not collude with the server.

Due to the global pandemic, the focus of most researchers around the world has changed to COVID-19 impact and recovery. In our work, we leverage the power of ORCID and other persistent identifiers to form a connected graph of scholarly communications to understand how organizations and countries are collaborating towards finding a pathway to COVID-19 recovery.Persistent identifiers (PIDs) allow us to link different entities like people, organizations, datasets, and publications by precisely identifying the participants within a research project. PIDs enable research provenance and attribution by accurately linking research entities.We leverage the interconnectedness of different identifiers to report on the formation of a collaboration network that captures connections between researchers, research datasets, publications, and grants. Using this newly formed collaboration network, we demonstrate how COVID-19 research collaboration has formed since late 2019.

A thesis submitted in total fulfillment for the degree of Doctor of Philosophy to the Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciences, College of Science, Health and Engineering, La Trobe University, Victoria.

A thesis submission in total fulfilment of the requirements for the degree of Doctor of Philosophy to the Research Centre for Data Analytics and Cognition, La Trobe Business School, College of Arts, Social Sciences and Commerce, La Trobe University, Victoria, Australia.

A thesis submitted in total fulfilment of the requirements for the degree of Doctor of Philosophy to the Research Centre for Data Analytics and Cognition, La Trobe Business School, College of Arts, Social Sciences and Commerce, La Trobe University, Victoria, Australia.

This table contains vocabulary and descriptions for a set of participant demographic information. It relates to code and data cited below. This vocabulary (and associated code and data) was used to test the interoperability solutions explored in the paper "Enhancing the interoperability of time series data through FAIR principles" (currently under review).Related code/data are:GOTTIMUKKALA, NIKHIL VARMA; JOUKHADAR, ZAHER; MICHALEWICZ, ALEKSANDRA; Schultz, Benjamin; Spreadborough, Kristal (2021): Time series data interoperability project - code. University of Melbourne. Software. https://doi.org/10.26188/16864636.v2 Schultz, Benjamin; GOTTIMUKKALA, NIKHIL VARMA; JOUKHADAR, ZAHER; MICHALEWICZ, ALEKSANDRA; Spreadborough, Kristal (2021): Time series data interoperability project - data. University of Melbourne. Dataset. https://doi.org/10.26188/16864630.v1

Presentation on RDM at La Trobe in 2020Both 15 minute version and 45 minute version includedSummary of the research data lifecycle with an emphasis on:- Principles- Support, tools & training available at La Trobe Uni

Comparing to traditional cartography, big data geographic information processing is not a simple task at all, it requires special methods and methods. When existing geovisualization systems face millions of data, the zoom function and the dynamical data adding function usually cannot be satisfied at the same time. This research classify the existing methods of geovisualization, then analyze its functions and bottlenecks, analyze its applicability in the big data environment, and proposes a method that combines spatial data structure and iterative calculation on demand. It also proves that this method can effectively balance the performance of scaling and new data, and it is significantly better than the existing library in the time consumption of new data and scaling

Presentation on Data Management at La Trobe in 2020Presentation slides, and recordingSummary of the data lifecycle, with a focus on library and professional staff uses.Includes:- Principles- Support, tools and training available at La Trobe Uni

Medications are designed to help keep older people well, but they can also have unintended adverse effects. Healthy older people may be more resilient to these adverse effects, but investigation is limited by the structure of available data (i.e. free text). This thesis develops the innovative data solutions required to collect and code medication data from healthy, community-dwelling older adults, and uses the resultant data to examine the risk profile of common potentially inappropriate medications and medications with anticholinergic properties.

We aim to transform scientific discovery and innovation in education through a scalable data infrastructure that bridges across the many disciplines now contributing to learning science (e.g., cognitive, social, and motivational psychology), discipline-based education research (e.g., Physics, Chemistry, Computer Science), and educational technology (e.g., intelligent tutoring, dialogue systems, MOOCs). The data infrastructure building blocks (DIBBs) we are developing and integrating are available online at learnsphere.org.

There are three original contributions for the analysis of temporal data, such as pedestrian counts, airline traffic, and economic indicators. The first new technique is for visualising data using a calendar layout, to study daily human activity. The second contribution is a new data abstraction which streamlines transformation, visualisation, and modelling. This is a data pluming infrastructure. The third contribution is to equip analysts with exploratory and explanatory tools for understanding missing patterns in time, in preparation for modelling. Three software packages are provided for practical use.

Due to the large volumes of spatial data, data is stored on clusters of machines that inter-communicate to achieve a task. In such distributed environment; communicating intermediate results among computing nodes dominates execution time. Communication overhead is even more dominant if processing is in memory. Moreover, the way spatial data is partitioned affects overall processing cost. Various partitioning strategies influence the size of the intermediate results. Spatial data poses the following additional challenges: 1)Storage load balancing because of the skewed distribution of spatial data over the underlying space, 2)Query load imbalance due to skewed query workload and query hotspots over both time and space, and 3)Lack of effective utilization of the computing resources. We introduce a new kNN query evaluation technique, termed BCDB, for evaluating nearest-neighbor queries (NN-queries, for short). In contrast to clustered partitioning of spatial data, BCDB explores the use of declustered partitioning of data to address data and query skew. BCDB uses summaries of the underling data and a coarse-grained index to localize processing of the NN-query on each local node as much as possible. The coarse-grained index is locally traversed using a new uncertain version of classical distance browsing resulting in minimal O( √k) elements to be communicated across all processing nodes.

The performance bottlenecks in modern data-intensive applications have induced database implementors to forsake high-level abstractions and trade-off simplicity and ease of reasoning for performance. Among the first casualties of this trade-off are the well-known ACID guarantees, which simplify the reasoning about concurrent database transactions. ACID semantics have become increasingly obsolete in practice due to serializable isolation – an integral aspect of ACID, being exorbitantly expensive. Databases, including the popular commercial offerings, default to weaker levels of isolation where effects of concurrent transactions are visible to each other. Such weak isolation guarantees, however, are extremely hard to reason about, and have led to serious safety violations in real applications. The problem is further complicated in a distributed setting with asynchronous state replications, where high availability and low latency requirements compel large-scale web applications to embrace weaker forms of consistency (e.g., eventual consistency) besides weak isolation. Given the serious practical implications of safety violations in data-intensive applications, there is a pressing need to extend the state-of-the-art in program verification to reach non- serializable data-intensive applications operating in a weakly-consistent distributed setting. This thesis sets out to do just that. It introduces new language abstractions, program logics, reasoning methods, and automated verification and synthesis techniques that collectively allow programmers to reason about non-serializable data-intensive applications in the same way as their serializable counterparts. The contributions xi made are broadly threefold. Firstly, the thesis introduces a uniform formal model to reason about weakly isolated (non-serializable) transactions on a sequentially consistent (SC) relational database machine. A reasoning method that relates the semantics of weak isolation to the semantics of the database program is presented, and an automation technique, implemented in a tool called ACIDifier is also described. The second contribution of this thesis is a relaxation of the machine model from sequential consistency to a specifiable level of weak consistency, and a generalization of the data model from relational to schema-less or key-value. A specification language to express weak consistency semantics at the machine level is described, and a bounded verification technique, implemented in a tool called Q9 is presented that bridges the gap between consistency specifications and program semantics, thus allowing high-level safety properties to be verified under arbitrary consistency levels. The final contribution of the thesis is a programming model inspired by version control systems that guarantees correct-by-construction replicated data types (RDTs) for building complex distributed applications with arbitrarily-structured replicated state. A technique based on decomposing inductively-defined data types into characteristic relations is presented, which is used to reason about the semantics of the data type under state replication, and eventually derive its correct-by-construction replicated variant automatically. An implementation of the programming model, called Quark, on top of a content-addressable storage is described, and the practicality of the programming model is demonstrated with help of various case studies.

Bayesian Network and Probability tables for Identatron. These are used for cluster sized thumbnail cache file fragment identification.

People spend majority of their time in indoor venues instead of the outdoor space. Hence, it is important to develop the navigation systems in indoor venues due to the increasing size while people have limited knowledge about indoor venues. This thesis studies the indexing and querying techniques in indoor venues that helps people to locate any point of interest (e.g. store and toilets) and support a better route planning.

This is a slide presentation (in Indonesian) for faculty-wide workshop on ZOTERO conducted at the Arts Faculty of Udayana University, Indonesia, on 23 October 2014.If you find it useful for your own ZOTERO workshop, please cite this repository by clicking the Cite button (the dark pink button on the top left above the title of this repository). The default citation style is DataCite.

The “Virtual Physiological Human: Dementia Research Enabled by IT” (VPH-DARE@IT) project aims to provide a systematic, multifactorial and multiscale modelling approach to enable improved diagnoses and prognoses of dementias. Whilst several tools are available for isolated tasks in the analysis of brain or brain-related data, a widely accepted solution to collect and harmonise, analyse, and organise data for the modelling of dementias is not yet available. The lack of such tools has been identified as a major challenge with high impact. The VPH-DARE@IT research platform aims to integrate the efforts of project partners and also of other open-source tools, which are already available and focus ondementia research, with the goal of providing a single framework where relevant anatomic-physiological representations (e.g. biomarkers & measures) are generated or co-ordinated to create more objective and predictive models of patients’ medical condition progression. The specific objectives of the research platform are to: Develop a workflow-oriented and extensible framework for clinical researchers indementia; Develop a workflow-oriented and extensible framework for VPH modellingresearchers in dementia; Define and implement automated pipelines for data processing in VPH-DARE@IT;  Define interoperability mechanisms for integrating the multi-faceted contributions ofproject partners into a single point of service; Re-use these interoperability mechanisms for leveraging functionality from otheropen source frameworks in dementia research; Support the interaction and integration of such frameworks with the VPH-Share datasharing infrastructure; Support data-provision centres in VPH-DARE@IT to federate their databases throughthe VPH-Share infrastructure.This deliverable aims to describe the architecture of the VPH-DARE@IT research platform, including its relationship with the other main software product of the project – the clinical platform. The approach taken for this document follows the ISO/IEC 42010-2011 standard “System and software engineering – Architecture description” as a guideline for its structure and contents.

User facilities are specialized government-sponsored research infrastructure available for external use to advance scientific or technical knowledge. Researchers compete for access to these facilities and specialized equipment, but do not consistently acknowledge the contributions made by facilities when they publish the results of their work. Collection of these research outputs is necessary to enable an accurate assessment of the scientific impact of these public investments. Recently several user facilities have begun to explore innovative approaches to these challenges, including adoption of ORCID. This report summarizes the discussions of the User Facilities and Publications Working Group, which focused on ascertaining what data would help agencies and facilities to map impact, and to determine whether and how ORCID could enable its collection in a manner that increases data capture and reduces reporting burden for researchers. The group had three objectives: to bring together publishers and facilities to better understand research, publication, and reporting workflows; to define terms to enable conversation; and to identify opportunities for working together to streamline and, where possible, automate impact reporting.

The slides from my presentation at Figshare Fest 2017

Tidy Data presentation from the Centre for eResearch Winter Bootcamp 2017.

We show that finite-order Markov models fail to capture long range dependencies that exist in human language and propose infinite-order non-Markovian (Bayesian and non-Bayesian) models which are capable of capturing unbounded dependencies. Presenting the structure of an infinite-order model amounts to a significant memory usage, and its very large space of parameters introduces computational and statistical burdens in the learning phase. We propose a framework based on compressed data structures which keeps the memory usage of modelling, learning, and inference steps independent from the order of the models. Our approach scales nicely with the order of the Markov model and data size, and is highly competitive with the state-of-the-art in terms of the memory and runtime, while allowing us to develop more accurate models.